Digital image processing for determining drop
sizes from irrigation spray nozzles
K.P. Sudheer
a,*, R.K. Panda
baScientist `B', National Institute of Hydrology, Deltaic Regional Centre, Siddhartha Nagar,
Kakinada 533 003, India
bAssistant Professor, Department of Agricultural Engineering, Indian Institute of Technology,
Kharagpur 721 302, India
Accepted 21 September 1999
Abstract
All the existing methods for measuring drop sizes produced by a sprinkler nozzle are either cumbersome, expensive or time consuming. Moreover, none could quantitatively express the relationship between drop size distribution and sprinkler head parameters viz. operating pressure and nozzle size. In the present study digital image processing technique has been applied to determine the drop size distribution from an irrigation spray nozzle. Image processing is the technique of automating and integrating a wide range of processes used for the human vision perception. The present study revealed that image processing technique can be successfully implemented for drop size measurement accurately. Being a novel technique, the method has some limitations for adaptation. These limitations can be very well contained through further research.
#2000 Elsevier Science B.V. All rights reserved.
Keywords:Sprinkler irrigation; Drop size; Image processing
1. Introduction
For any sprinkler irrigation system, there is no direct evaluation procedure available to assess the system performance analytically. The evaluation is generally done with the support of field experiments. The main reason for this is that the droplet sizes produced by any sprinkler nozzle have a significant effect on the uniformity of application. These
*Corresponding author. Tel.:91-884-372254; fax:91-884-350054.
E-mail address: [email protected] (K.P. Sudheer)
drop size characteristics are functions of nozzle size and operating pressure. Solomon et al. (1985) indicated that although a few researchers have published measured data on the drop size distribution, none could quantitatively express the relationship between nozzle size, operating pressure and the drop size distribution. One of the reasons for this may be non availability of a technique to measure the drop size accurately.
A sprinkler of a given nozzle size and trajectory angle, operating at a constant water pressure would produce a particular range of drop sizes. The dispersing action of the jet which leads to drop formation is due to the turbulence and air friction drag (Kohl, 1974). The drop size from spray nozzles is an important factor affecting the formation of seals on soil surfaces that restricts infiltration. Because small drops possess less energy, when they impact the soil surface, seal that limit infiltration form more slowly than larger drops. For these reasons, it is sometimes possible to reduce runoff and erosion by converting from sprinklers that emit large drops to ones with smaller drops. Drop size is especially important when sprinklers operate in winds. Distribution patterns from sprinklers that emit smaller drops are more subject to wind distortion and lower application uniformity. In addition, increased losses due to wind drift usually occur with small droplet sprinklers (James, 1988).
Measurement of drop sizes, produced by a sprinkler nozzle, is also important in quantitatively expressing the relationship between the drop size distribution and the sprinkler head parameters viz. operating pressure and nozzle size. Such a relation can further be helpful in developing an analytical model to evaluate the system performance. Measurement of drop sizes can further be used to evaluate the impact of the water drops on the soil. Direct and accurate measurement of the drop size is possible, but the equipment required for such a procedure is highly sophisticated and expensive.
1.1. Existing techniques
The stain method (Seginer, 1963), based on the assumption that a drop falling upon a uniform absorbent surface produces a stain whose diameter is proportional to the diameter of the drop, has been in use since the early 1940s. The distribution of drop sizes is determined by comparing the size of the stain with those produced by drops of a known diameter. Hall (1970), by experimenting with many absorbent surfaces, cautioned the potential users of this technique stating that care must be taken to ensure that drops fall over at a sufficient distance to attain their terminal velocities before striking the absorbent surface.
The photographic method (Hoffman, 1977) has the advantage of being a direct measurement technique to determine the size of individual drops. But the equipment required to make the measurement is too cumbersome for field use. Besides, visual interpretation techniques have certain disadvantages. They require extensive training and are labour intensive (Lillisand and Keifer, 1987). In addition spectral characteristics are not always fully evaluated in visual interpretation efforts. This is because of the limited ability of the eye to discern total values on an image and the difficulty for an interpreter to simultaneously analyze numerous spectral pattern.
rainfall and the water drops form a spherical shape with flour on the surface. This is then dried in oven and the diameter of the dried pellet is measured. Calibration charts prepared from water drops of known diameter are used for determining the drop sizes from the rainfall. Laws found that the calibration curve varied slightly from one bag of flour to the next bag of the same brand of flour. They also stated that the calibration curves for smaller diameter drops were difficult to obtain.
The momentum method, that includes pressure transducers and piezo electric sensors, has been successfully used to measure rain drop size. However, they have most commonly been used to measure rainfall energy since they aggregate the effects of multiple drops striking a finite surface area.
Tate (1961) developed the immersion technique by collecting water drops in a low density, immiscible liquid (oil). The oil envelops the drops preventing both evaporation from the drop as well as any condensation on the drop. Owing to the higher force of surface tension water drops form a spherical shape, diameter of which can be measured by using a measuring microscope or any similar equipment.
All these techniques are either cumbersome, or require expensive and sophisticated equipment. Keeping in view the drawbacks of existing techniques, a study was conducted to determine the feasibility of using digital image processing for measuring the drop sizes from a sprinkler nozzle.
1.2. Image processing technique
Image processing is the enterprise of automating and integrating a wide range of processes and representation used for vision perception. Computer vision research often deals with relatively domain independent considerations. The results are useful in a wide range of contexts. Usually such work is demonstrated for one or more application areas (Deluitche et al., 1990; Liao et al., 1990). In general, an image processing technique consists of three steps viz. data acquisition, processing and interpretation.
1.3. Data acquisition
The first step in the vision process is image formation. Images may arise from a variety of techniques. For example, most television based systems convert reflected light intensity into an electronic signal which is then digitized on requirement. In the present study, image formation has been done by taking photographs of the sprinkler droplets and digitizing it using a scanner.
1.4. Processing and interpretation
spatial redundancy. Neighboring pixels in the image have the same or nearly same physical parameters. A collection of techniques which is called `processing', exploits the redundancy to undo the degeneracies in the imaging process. Once the digitized image is processed, the information can be interpreted, based on the objective, using several techniques.
2. Methodology
Photographs of sprinkler droplets, in flight, were taken using a high resolution, high speed camera and digitized using a scanner. The photograph obtained from a SLR camera, fixed on a stand, has been converted into a digital image using a CCD (charge couple device) camera connected to a MVP/AT computer system. Thus the scene is converted to a two dimensional array of grey values. A sample digitised image is presented in Fig. 1.
Segmentation is done on the digitised image to partition an image into regions which correspond to the object of interest. Thresholding is the most popular segmentation method. It is important in a digital image processing to select an adequate threshold of gray level for extracting objects from its background. In ideal case, the gray level histogram has a deep and sharp valley between two peaks representing objects and background respectively, so that the threshold can be selected at the bottom of the valley.
Segmentation leads to a binary image consisting of white objects (water drops) in a black background. In the next step, one may measure the dropsize and count by extracting the region and computing its parameters.
Several techniques are available for developing the regions out of the segmented image. In the present study `pixel aggregation technique' has been used, being a simple one. The approach starts with a set of seed points and thereafter, regions are grown by appending to each seed point those neighboring pixels having similar grey values.
Once all the regions (water drops) in the image are developed, the area of each region is found by computing the number of pixels in the region. The number of pixels can be converted into actual area using the resolution and scale of the actual image. The perimeter of the regions are also computed in a similar way by counting the number of pixels in the object region with at least one neighbour pixel in the background region.
2.1. Shape identi®cation
It is important to determine the shape of the region to confirm if it belongs to the category of the object of interest. The compactness ratio was used as an index to identify the shape of region in the present study. Digital images, being a two-dimensional descrete matrix of pixel grey values, can not reproduce the Eucledian geometry of the nature. Thus, a circle will be a concentrated collection of square boxes (pixels) in the digital image and will not have the ideal compactness value of 1/4p. Moreover, as the shape of water drops moving through air at high speed need not be exact circle and will be distorted, the compactness value will be around 1/4p. Thus, a tolerance range became necessary. If the image is highly noisy and shapes are distorted, a larger tolerance limit may be advisable. In our experiment, scenes were of moderate complexity and we chose the tolerance limit as 1/12p. Though this parameter may influence the final size/shape distribution, selection of threshold should be made based on the complexity of the scene and presence of distortion in the scene, which can be observed by examining the grey level histogram.
The compactness ratio is defined as,
compactness area
perimeter2 (1)
In Eq. (1), the area corresponds to the number of pixels falling in the object region (water drop) and perimeter corresponds to the number of pixels in the object region with an adjacent neighbouring pixel in the background.
2.2. Drop size determination
Droplet sizes are computed, assuming that the region is circular, from the value of its area as,
3. Results and discussions
The digitized image for the sprinkler nozzle discharge was prepared in a similar procedure discussed above. Thresholding depends on the characteristics of the grey level histogram (Fig. 2). This histogram depicts the number of pixels with each grey value in the range 0±255. When the gray level histogram of the image was studied, it was observed that there is very little variation between the gray level of all pixels (corresponding to the object as well as background). Hence, it was difficult to separate the object from its background by searching a valley. This difficulty in delineation of object and background led to division of the image into different grids (windows), thresholding individual grid separately, and then integration of all the segmented grids for further analysis. Since the threshold selection based on the valley in the histogram was not feasible, a method proposed by Otsu (1979) was used for each grid. The method is characterized by its non-parametric and unsupervised nature of threshold selection. The method utilizes the zeroth and first order cumulative moments of the gray levels and an optimal threshold is selected automatically and suitably. It avoids the consideration of a local property such as valley, but considers the integration of the histogram.
The area of each region was determined using region grow algorithm which works based on the principle of pixel aggregation. The criterion for the identification of the region was fixed as a gray level value of 255 (perfect white). Since gray level value of all objects (water drops) were made to 255 during thresholding, any region identified should fall in the category of water drops. Confirmation of the identified object, if it falls in the category of interest, was done by computing the compactness ratio of each region using Eq. (1).
In the process of identification of shape, a tolerance limit of1/12pto the computed compactness was selected. All those regions whose compactness ratio fell within this range was considered as a water drop.
The results of the image processing technique for droplet size determination for a sprinkler of 5 mm diameter nozzle at an operating pressure of 14 m of water is presented Table 1
Output of image processing technique
Area (mm2) Perimeter (mm) Compactness Diameter (mm)
0.73533 3.09880 0.07658 0.96760
1.41066 4.52345 0.06894 1.34019
4.23202 7.22370 0.08110 2.32129
4.93732 8.40800 0.06984 2.50727
7.05337 9.99180 0.07065 2.99677
7.75871 10.34000 0.07257 3.14304
8.46400 10.32400 0.07941 3.28279
9.16936 11.55120 0.06872 3.41684
9.87472 12.01529 0.06840 3.54583
11.28534 12.81280 0.06874 3.79064
11.99072 13.49200 0.06587 3.90731
13.40099 14.31460 0.06540 4.13070
16.21685 14.30197 0.07928 4.54400
in Table 1. This table depicts the area, perimeter alongwith the computed compactness of the dropsizes corresponding to each of the region obtained.
The resulted distribution of droplets from the image processing technique has been compared with the measured data. The pellet method was used to measure the drop size in laboratory for comparative study. The comparative plot of drop size distribution resulted from image processing technique and pellet method for a sprinkler nozzle of 5 mm diameter operating at a pressure of 14 m is presented in Fig. 3. It can be observed from the plot that the smaller dimension drops (below 0.90 mm) were not computed by the presented technique. Further examination of the results (Fig. 3) revealed that the drops having diameter of about 0.90 mm have increased a great deal in number when compared to actual data. This may be due to the fact that the resolution (the range of resolution depends on the hardware configuration of the computer) taken for the digital image was only 256256 pixels and each pixel corresponds to an actual size of 0.8390.839 mm. This size of pixel is large when compared to the size of smaller dimension drops. Any region having an area below that of a pixel was considered having an area equal to that of a pixel and the computations were performed accordingly. Hence it can be concluded that for computer vision technique to be used for determining smaller drop sizes, the resolution of pixel should be taken in such a way that the least possible dimension of the drop falls below it. In other words, higher the resolution of the digital image, greater is the accuracy of the technique.
4. Summary and conclusion
The feasibility of image processing technique for determination of the drop sizes from an irrigation spray nozzle was studied. The photographs of the droplets on fly were taken using an ordinary camera and analyzed using the digital image processing technique. The technique performed reasonably well in determining the drop size distribution of water spray from irrigation nozzles. Being a novel investigation, the method has some limitations for adoption, which can be rectified through further research. This technique may be much useful for researchers to ascertain a quantitative relationship between the drop size distribution, operating pressure of sprinkler and its nozzle diameter.
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